!21 solve DI: [MS][MindArmour][Doc]EnsembleAdversarialDefense format is...

!21 solve DI: [MS][MindArmour][Doc]EnsembleAdversarialDefense format is defferent with others  https://gitee.com/mindspore/dashboard/issues?id=I1GSW0
Merge pull request !21 from ZhidanLiu/master

mindarmour/defenses/adversarial_defense.py

@@ -23,7 +23,7 @@ from mindspore.nn import SoftmaxCrossEntropyWithLogits
 from mindspore.nn import WithLossCell, TrainOneStepCell
 
 from mindarmour.utils._check_param import check_pair_numpy_param, check_model, \
-     check_param_in_range, check_param_type, check_param_multi_types
+    check_param_in_range, check_param_type, check_param_multi_types
 from mindarmour.defenses.defense import Defense
 
 
@@ -166,4 +166,36 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
         return loss.asnumpy()
 
 
-EnsembleAdversarialDefense = AdversarialDefenseWithAttacks
+class EnsembleAdversarialDefense(AdversarialDefenseWithAttacks):
+    """
+    Ensemble adversarial defense.
+
+    Args:
+        network (Cell): A MindSpore network to be defensed.
+        attacks (list[Attack]): List of attack method.
+        loss_fn (Functions): Loss function. Default: None.
+        optimizer (Cell): Optimizer used to train the network. Default: None.
+        bounds (tuple): Upper and lower bounds of data. In form of (clip_min,
+            clip_max). Default: (0.0, 1.0).
+        replace_ratio (float): Ratio of replacing original samples with
+            adversarial, which must be between 0 and 1. Default: 0.5.
+
+    Raises:
+        ValueError: If replace_ratio is not between 0 and 1.
+
+    Examples:
+        >>> net = Net()
+        >>> fgsm = FastGradientSignMethod(net)
+        >>> pgd = ProjectedGradientDescent(net)
+        >>> ead = EnsembleAdversarialDefense(net, [fgsm, pgd])
+        >>> ead.defense(inputs, labels)
+    """
+
+    def __init__(self, network, attacks, loss_fn=None, optimizer=None,
+                 bounds=(0.0, 1.0), replace_ratio=0.5):
+        super(EnsembleAdversarialDefense, self).__init__(network,
+                                                         attacks,
+                                                         loss_fn,
+                                                         optimizer,
+                                                         bounds,
+                                                         replace_ratio)